Major limitations in drug development are identification of cellular targets (and off-targets) for novel drugs, and discovery of effective drg combinations to combat rapidly evolving diseases such as cancer. Furthermore, drug screens require extensive handling, which limits throughput and increases variability. Finally, genetic background often determines drug susceptibility, complicating selection of optimal treatment regimens. This proposal describes a broad effort in my lab to begin to address these issues by (1) dramatically enhancing the speed, accuracy, and scalability of pooled genome-wide screens using bioreactors to automate cell culture, (2) developing tools to measure pairwise genetic interactions between drug targets in high-throughput, and (3) using these tools to identify synthetic lethal combination targets that are specific to stress and oncogene states. We have shown in pilot studies that our novel high-complexity shRNA libraries (25 shRNAs/gene) can be used to identify the target of a cancer-killing drug with remarkable specificity (Matheny et al., 2013). We have also developed a scalable, rapid strategy to create double-shRNA libraries to simultaneously measure genetic interactions between 100,000's of gene pairs (Bassik et al., 2013). We will adapt this platform to create novel, high-complexity gene modulation libraries composed of shRNA and CRISPR elements targeting pairs of FDA-approved drug targets, and then identify synthetic lethal interactions between these genes in the context of a panel of stresses and oncogenes. Elucidating these synergies will inform our understanding of the underlying biology of stress signaling and cell death regulation, and illuminate new therapeutic combinations using available drugs in diseases such as cancer where these stresses are prevalent. At the same time, the genetic interaction maps will allow us to directly investigate off target effects of drugs, which would streamline the identification of viable candidate molecules for further development. Together, we expect the tools developed here will be broadly useful for functional genomics screening and drug development efforts, and that this work will establish a new paradigm for empirical testing and identification of drug response biomarkers.